0
$\begingroup$

I want to create some model of human behavior. Basically - it's expected to answer for question if some particular user will agree or not agree for some action. Feature list for it is: user_id interacting_user_id some_normalized_value some_enumerated_value ....

Supposing usage of NN, how to standardize user_id and interacting_user_id?

This model is supposed to be used in system when number of possible values for user_id and interacting_user_id will be increasing over the time, to possibly quite big numbers. Is there any better option than creating separate pair of input layer neurons for each user?

Reason for putting user ids into features:

Let's suppose, that this is about trading service and we want to predict probability of doing business for some pair of users under certain conditions (date, price, .....).

I have not many information about particular user, so it would be hard to get some features like age, sex, or others, especially when it's hard to determine how relevant they are to the result. I suspect, that there is big variance between different users. I suspect there are some social interactions between users, so some specific pairs of users can produce significantly different results than average due to their specific relationship. Most important - I want to combine somehow knowledge about population behavior with knowledge about specific user's behavior. So for example - if there is no knowledge about specific user model will predict using population knowledge, as soon as some behavior history will be recorded it'll start to use more user specific data.

Improve this question
$\endgroup$
3
  • $\begingroup$ The user id should play no role in predictions. This is absurd! Features describing a user is what your neural network is trained on. $\endgroup$
    – Arun Jose
    Commented Oct 4, 2016 at 12:13
  • $\begingroup$ That's not necessarily true, @Arun. If there's information contained in the user id (e.g. location or age of the user), and that information is not yet captured by any features, could certainly use user id. $\endgroup$
    – boot-scootin
    Commented Oct 4, 2016 at 13:46
  • $\begingroup$ I explained why there is the user id. $\endgroup$
    – piotrpo
    Commented Oct 4, 2016 at 14:37

1 Answer 1

Reset to default
2
$\begingroup$

Basically, you want to use both categorical variables (id) and some continious standardized variables. This similar question gives insight in this issue. But more importantly, why do you want to include id? Do you expect multipe observations per id? And do you expect the user id to have any predictive value?

Improve this answer
$\endgroup$
8
  • $\begingroup$ I explained why there is the user id. $\endgroup$
    – piotrpo
    Commented Oct 4, 2016 at 14:37
  • $\begingroup$ So, do you expect of have multiple observations of the same id? If not, there is no value in having id in your model. It's a categorical thing and there is no information in the category itself unless the user shows similar behavior in multiple observations. Also, how do you expect your model to behave when a new user (with an id, unkown to the model) enters the model. $\endgroup$
    – Ivo
    Commented Oct 4, 2016 at 14:41
  • $\begingroup$ Yes I expect to have multiple observations ot the same id. However - I'm not sure that there will be some observation at all for some specific id and it's sure that there will not be enough observations for specific id to cover "all" situations represented by rest of features. $\endgroup$
    – piotrpo
    Commented Oct 4, 2016 at 14:53
  • $\begingroup$ In that case. Check the link in my answer. $\endgroup$
    – Ivo
    Commented Oct 4, 2016 at 14:58
  • $\begingroup$ Thanks, I just wondered if there is a way to avoid creating 1M feature wide model. $\endgroup$
    – piotrpo
    Commented Oct 4, 2016 at 15:07

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.